The present system is related to learning systems and big data analytic engines.
Nonnegative matrix factorization and completion (NMFC), which aims to approximate a (partially) observed data matrix with two nonnegative low rank matrix factors, has been successfully applied in a wide range of machine learning applications, such as dimensionality reduction, collaborative filtering, compressed sensing. Nevertheless, due to the non-convex formulation and the underlying inverse problem, many existing solutions are not accurate and not scalable to handle large problems.